Crop grading via deep learning

ABSTRACT

Methods and systems for crop grading and crop management. One or more images of crops are obtained and one or more crop related features are at least one of identified or extracted from the one or more images. A crop health status is determined based on the one or more crop related features, an environmental context, a growth stage of the crop, and a farm cohort by using a computerized deep learning system to perform an automated growth stage analysis. One or more actions are at least one of recommended, triggered, and performed.

BACKGROUND

The present invention relates to the electrical, electronic, andcomputer arts, and more specifically, to techniques for crop grading andcrop management.

Crop quality assessments are important for managing risks during thegrowing season and enabling price premiums after harvest. Across somedeveloping regions of the world, quality evaluations are manual, ofteninconsistent, and much of the specialty equipment required toconsistently collect and analyze images, such as photographic images, inthe field are inaccessible to the average small-scale farmer. As aresult, productivity suffers, and farmers fetch lower prices in themarket and might have trouble getting loans in the future. In addition,there is an absence of uniform guidelines for farmers to use on how togrow crops.

SUMMARY

Principles of the invention provide techniques for crop grading via deeplearning. In one aspect, an exemplary method includes the operations ofobtaining one or more images of crops; at least one of identifying orextracting one or more crop related features from the one or moreimages; determining a crop health status based on the one or more croprelated features, an environmental context, a growth stage of the crop,and a farm cohort by using a computerized deep learning system toperform an automated growth stage analysis; and at least one ofrecommending, triggering, and performing one or more actions.

In one aspect, an exemplary system for determining or predicting a gradeof a crop comprises a memory; and at least one processor, coupled to thememory, and operative to obtain a report regarding a first grade of acrop from a user; determine a second grade of the crop from an expertsystem using deep learning to analyze weather conditions and one or moreimages of the crop; determine an optimal time to harvest the crop; andassess an impact of the crop grading on a predicted market value of thecrop.

In one aspect, an exemplary non-transitory computer readable mediumcomprises computer executable instructions which when executed by acomputer cause the computer to perform the method of obtaining one ormore images of crops; at least one of identifying or extracting one ormore crop related features from the one or more images; determining acrop health status based on the one or more crop related features, anenvironmental context, a growth stage of the crop, and a farm cohortusing deep learning to perform an automated growth stage analysis; andat least one of recommending, triggering, and performing one or moreactions.

As used herein, “facilitating” an action includes performing the action,making the action easier, helping to carry the action out, or causingthe action to be performed. Thus, by way of example and not limitation,instructions executing on one processor might facilitate an actioncarried out by instructions executing on a remote processor, by sendingappropriate data or commands to cause or aid the action to be performed.For the avoidance of doubt, where an actor facilitates an action byother than performing the action, the action is nevertheless performedby some entity or combination of entities.

One or more embodiments of the invention or elements thereof can beimplemented in the form of a computer program product including acomputer readable storage medium with computer usable program code forperforming the method steps indicated. Furthermore, one or moreembodiments of the invention or elements thereof can be implemented inthe form of a system (or apparatus) including a memory, and at least oneprocessor that is coupled to the memory and operative to performexemplary method steps. Yet further, in another aspect, one or moreembodiments of the invention or elements thereof can be implemented inthe form of means for carrying out one or more of the method stepsdescribed herein; the means can include (i) hardware module(s), (ii)software module(s) stored in a computer readable storage medium (ormultiple such media) and implemented on a hardware processor, or (iii) acombination of (i) and (ii); any of (i)-(iii) implement the specifictechniques set forth herein.

Techniques of the present invention can provide substantial beneficialtechnical effects. For example, one or more embodiments provide one ormore of:

establishing a cohort history of crop health and quality by translatingphotographic image evaluation results to crop grading at each growthstage of the crop;

using visual analytics to determine the crop health status of the plantin an effort to anticipate quality;

modeling a crop blueprint over the growth stage of a crop by relying ona machine-mediated network (e.g., a machine-mediated human evaluationnetwork using a set of so-called “Mechanical Turks”) to semi-automatethe process of expanding the set of classes in a visual classifier;

using databases of farms with similar characteristics, contextualinformation, or both to determine the crop grading;

predicting the optimal time to harvest the crop based on establishedcohort history of crop health and quality; and

analyzing the market demand and price for the crops.

These and other features and advantages of the present invention willbecome apparent from the following detailed description of illustrativeembodiments thereof, which is to be read in connection with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts a cloud computing environment according to an embodimentof the present invention;

FIG. 2 depicts abstraction model layers according to an embodiment ofthe present invention;

FIG. 3A is a graph of an example crop blueprint for a tomato crop, inaccordance with an example embodiment;

FIG. 3B is an illustration of an example system for analyzing andmanaging crops, in accordance with an example embodiment;

FIG. 4 is a flowchart of an example method for generating or updating acrop blueprint, in accordance with an example embodiment;

FIG. 5 is a flowchart of an example method for determining or predictinga grade of a crop, in accordance with an example embodiment; and

FIG. 6 depicts a computer system that may be useful in implementing oneor more aspects and/or elements of the invention, also representative ofa cloud computing node according to an embodiment of the presentinvention.

DETAILED DESCRIPTION

Generally, systems and methods for crop grading via deep learning aredisclosed. A health status of a crop is determined, and a sequencing andtiming of the most suitable set of options for the crop is determinedbased on the health status. Weather, plant status, soil conditions,visual evidence, reports from experts, and farmer profile are some ofthe criteria used to determine the health status of crops, and thesequencing and timing of the most suitable set of options for the crop.The assessment of the crop health status is based on an analysis of cropfeatures, crop stress levels, crop images (such as photographic images),environmental context (such as weather), crop knowledge graph models,and the like. In one example embodiment, a plurality of humans withinterconnection facilitated by a communication network submit reports tothe system regarding the observed crop health and crop status, initialconfidence levels (as described more fully below), and the like.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

Referring now to FIG. 1, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 includes one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 1 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 2, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 1) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 2 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and a service 96 providing crop grading viadeep learning; in some instances, human mediated crop grading and cropmanagement via human observations and deep learning 96. In one or moreembodiments, the module 96 learns through implementation of a neuralnetwork that is supervised by human evaluators using a set of so-called“Mechanical Turks.” Generally, a neural network includes a plurality ofcomputer processors that are configured to work together to implementone or more machine learning algorithms. The implementation may besynchronous or asynchronous. In a neural network, the processorssimulate thousands or millions of neurons, which are connected by axonsand synapses. Each connection is enforcing, inhibitory, or neutral inits effect on the activation state of connected neural units. Eachindividual neural unit has a summation function which combines thevalues of all its inputs together. In some implementations, there is athreshold function or limiting function on at least some connectionsand/or on at least some neural units, such that the signal must surpassthe limit before propagating to other neurons. A neural network canimplement supervised, unsupervised, or semi-supervised machine learning.

As described above, a health status of a crop is determined, and asequencing and timing of the most suitable set of options for the cropis determined based on the health status. Weather, plant status, soilconditions, visual evidence, reports from experts and other observers,and a farmer profile are some of the criteria used to determine thehealth status of the crops, and the sequencing and timing of the mostsuitable set of options for the crop. The assessment of the crop healthstatus is based on an analysis of crop features, crop stress levels,photographic images of the crop, environmental context (such asweather), crop knowledge graph models, and the like.

One example embodiment employs one or more photographic images of thecrop, and includes identifying or extracting one or more features fromthe images (such as a size, a shape, a color, visual spots,virus/insects, and the like), determining the crop health status usingthe features and environmental context (such as the weather, a locationof the crop, and the like), a farm cohort (such as planting history ofthe farm, a water uptake history of the farm over the last N days, pastfarmer activities, and the like), assessing or predicting the quality Qof the crops within a threshold risk R (such as Q is less than theexpected quality Q(s) for the growth stages S), and, based on theestimation or prediction, the system generates or updates both a cropblueprint and alerts for members of a human network.

In one example embodiment, crop quality cohorts (CC) are establishedover the growth stage of a crop. The crop quality cohorts may beaugmented by a machine-mediated human evaluation network to assess cropquality and health over time. More than one evaluator confirms thequality rating based on their understanding of weather, soil fertility,farm management practices associated with a photographic image, and thelike. This rating is used to inform the farmer of critical productionrisks, to support farmer differentiation and rating at harvest, and thelike.

Using the tomato as an example, the USDA Grade standard for tomatoesdescribes requirements for size, color, firmness, and damage (see Choi,K. H. G. H, et al. “Tomato maturity evaluation using color imageanalysis. ” Transactions of the ASAE 38.1 (1995): 171-176.). Thesefeatures are typically assessed at harvest via visual assessment orusing laboratory analysis. These methods, however, have often proved tobe time consuming, inefficient, and destructive. Also, highly trainedand qualified technicians are required for such detection techniques. Inpractice, in some locations, buyers from small farms make a visualassessment of the quality and determine the fruit's suitability for themarkets they serve. Local tomatoes sold on the road side are often smalland malformed.

Continuing with the above tomato example, most consumers measure thequality of a tomato primarily by three factors: physical appearance(such as color, size, shape, defects, decay, and the like), firmness,and flavor. Fruit quality is significantly affected by the stage ofripeness of the tomato when removed from the plant, the number of timesthe tomato is handled, the storage temperature and storage time, and thelike. The longer the tomato remains on the plant, the more flavorful thefruit is. The less amount of handling, the smaller incidence of bruising(some have suggested that flavor is also reduced with increasedhandling). It is frequently observed that tomato fruit that is notripened on the plant does not have the same flavor and aroma as fruitthat has developed its red color (or final fruit color) on the plant.

It is also known that the growing conditions for tomatoes can influencethe best time of harvesting and the quality of fruit produced atharvest. For example, deficit irrigation (where only, for example, 50 to60 percent of the crop's water requirements are met) leads to smallerbut redder fruit. On the other end of the spectrum, rain shelters areoften used to protect open field tomatoes since heavy downpours alsoreduce yield and increase the potential for disease. Many fungaldiseases, for example, reside in the soil or in bits of plant materialleft over from previous years. When it rains, fungal spores splash uponto the lower leaves of a tomato plant, infecting them. The next timeit rains, the spores from the infected leaves splash up onto the nextset of leaves. Unchecked, the infection may spread all the way to thetop of the plant.

Early blight (Alternariasolani) and late blight (Phytophthorainfestans)are the two common fungal diseases of tomato plants. When these fungiinfect tomato leaves in a favorable environmental condition, symptomscan rapidly spread and cover the entire leaf blade. Identifying diseasedleaves as early as possible is one of the most cost effective approachesto protect the tomato plant and its future harvest. Xie et aldemonstrated the potential for early blight detection usinghyperspectral imaging via a high quality camera (see Chuanqi Xie et al.Detection of early blight and late blight diseases on tomato leavesusing hyperspectral imaging, Sci Rep. 2015; 5: 16564. Published online2015 Nov. 17). Reflectance of healthy samples was higher than that ofinfected ones in the near-infrared region (750-1000 nm), which is causedby the collapse of leaf cell structure as the disease spreads. The meanspectral reflectance curves of healthy, early blight and late blightdiseased leaves illustrate the potential for image analytic techniquesto provide early warning of diseases or other environmental conditionsthat may impact the crop grade at harvest. In one example embodiment,image evaluation results are analyzed and translated to a measure oftheir impact on crop grading at each growth stage of the crop in orderto establish a history of crop health and quality. This may beaccomplished using hyperspectral imaging, normal photographic imaging,and the like. In one example embodiment, high resolution, conventionalphotographic images of the crop (such as leaves and tomatoes on theplant) can be analyzed to assess insect infestation, tomato size,symmetry, and other attributes.

The use of water uptake patterns to determine crop health status in aneffort to predict productivity is known. In one example embodiment,visual analytics are used to determine the crop health status of theplant in an effort to anticipate and improve quality. For example, plantleaves typically get infected first with a fungal disease, and then thedisease spreads to the plant fruit. Visual analytics of photographicimages of the crop can be used to detect the disease in its earlystages. In one example embodiment, the images are taken at multiplelocations and at multiple magnifications from, for example, anaeronautical drone. The images are then compared to images taken at thesame location (as determined, for example, by the Global PositioningSystem) and magnification to those obtained at an earlier time.

In one example embodiment, the assessment of crop cohort (CC) mayinclude an automated growth stage analysis by applying one or moremachine learning methods such as deep learning and neural net with someconfidence level (L) (that may use context information, e.g., weather).In case the confidence level L is less than a certain threshold, thesystem may video stream photographic images of the crop to a remoteprofessional for expert evaluation. The initial confidence level can bedetermined by the human expert. Over time, as historical data isaccumulated, the system may automatically configure L based onhistorical values. The remote professional might be an expert in growingor harvesting the crop such as an agricultural professor, a so-called“Mechanical Turk,” an individual skilled in crop cohort assessment, andthe like.

In one example embodiment, an image, such as a photographic image, of acrop is captured and submitted to a crop analytics system. Photographicimages may be obtained using a mobile phone/device, a fixedcamera/sensor, a sensing device mounted on a farm vehicle, a flyingdrone, a satellite, and the like (image source generally depicted as app304). Photographic images may be submitted at regular time intervals, atregular intervals of the crop growth stage, in response to an event(such as a specified rate of change of the crop color), or in acontinuous manner. Some embodiments take photographic images atnon-regular intervals, triggered by events such as the rate of change ofcrop color. For example, there may be no need to take photographs everythird day when the leave color is not changing. When the leaves or cropsare changing color rapidly (e.g., from green-to-brown), then it may bean early indication of disease, insect infestation, and the like, andphotographs should be taken more closely together (e.g., once every fourto six hours) to ensure the efficacy of pesticide application, changesin watering patterns, and the like. Once the rate of color change hasstabilized, then the photographic images can be taken at the previousrate (e.g., at regular intervals).

The photographic images may contain various degrees of magnification sothat the small scale (e.g., insects on leaves) or large scale (e.g., theentire farm) can be captured and analyzed. The growth stages of a cropvary from crop to crop type. For example, for a tomato, the stages are:establishment, vegetative growth, flowering, fruit set, and maturity.

The identification or extraction of features from photographic images ofthe crop is based on one or more machine learning methods that usevisual analytics, deep learning, and the like. Example features include,but are not limited to, a shape of the crop, a color of the crop, a sizeof the crop, visual spots on the crop, a virus of the crop, worms orinsects infesting the crop, and the like. In one example embodiment, afeature analyzer detects and extracts a shape of the crop, identifiesthe rate of change of crop color, detects one or more spots on the cropand their patterns, and the like. In one example embodiment, ahealth/quality assessor identifies, assesses, and characterizes thenature of the insect or virus infecting the crop at various locationswithin the farm. In one example embodiment, the health/quality assessordetermines a pattern of worms or insects and identifies how they arespreading to other plants in the farm over time via a time series ofphotographic images. This data could be used to alert the farmer to pickthe healthy plants early or to spray pesticides on the healthy crops toavoid the damage that would be caused by the spread of the worms orinsects to the healthy plants.

As described above, the assessment of the crop health status is based onthe analysis of crop features, crop stress levels, environmental context(such as weather), crop knowledge graph models, reports by observers,and the like.

FIG. 3A is a graph of an example crop blueprint 390 for a tomato crop,in accordance with an example embodiment. Each column of the cropblueprint 390 corresponds to a growth stage of the crop. In one exampleembodiment, the growth stages are establishment, vegetative growth,flowering, fruit set, and maturity. The first row of each columncorresponds to a question regarding, for example, a state of the crop.If the question is answered in the affirmative, the second row of eachcolumn provides detailed information, such as a diagnosis of a healthissue and a recommended action. The third row of each column providesrecommended inputs, such as recommended environmental conditions. Thefourth row of each column provides an illustration of the crop at thecorresponding growth stage.

The assessment of the health of the crop can use a decision treeinduction technique to generate new classification rules based on ananalysis of crop features obtained by monitoring crops and theirproperties, where each rule set is given as a classifier. Modelsgenerated from decision trees are stored in a graph-based knowledgerepresentation for easy interpretation as it also uses ontology to modelcontextual information. For example, mature red tomatoes will retain ahigh quality for approximately four to seven days if stored at 90 to 95percent relative humidity and at a temperature of 46° F. to 50° F. Fruitborers (insects), however, cause up to 70 percent of fruit loss.Therefore, this may indicate that, when the crop is infested with fruitborers, either the fruit should be picked on time or early to avoid theloss of the fruit. If the health or quality of the crop drops from anexpected level L for the current stage of crop growth, various actionsare recommended, triggered, performed, or any combination thereof by thesystem. Examples of actions include: recommending that the farmer pickthe crops early, if possible; recommending to spray pesticides or otherchemicals; recommending the capture of photographic images at a finerdetail to see, for example, if the insect infestation is expanding;recommending to switch to a different crop the following growing season;and the like. One or more embodiments further include carrying out therecommended actions, such as applying pesticides and the like. Forexample alerts, messages, and the like can be sent over network adaptor20 discussed below to trigger performance of actions.

In one example embodiment, a grade of a crop is determined or predicted.The grade may be determined or predicted, for example, just prior toharvesting. The determination of crop grading can be performed by ahuman expert (such as an agronomist), via collaboration with humanobservers, for example using a team of so-called “Mechanical Turks,” viareports from crop/farm aggregators, by an expert system, and the like.In one example embodiment, the system provides a video stream (or aseries of photographs) to a remote professional for evaluation of thecrop. In one example embodiment, the crop grading also assesses theimpact of the grading on the predicted market value of the crop, takinginto consideration the local market conditions (supply and demand) inlocations where the crops are expected to be sold.

In one example embodiment, the method of determining or predicting thecrop grading and determining or predicting the optimal time to harvestthe crop utilizes learning algorithms and databases of neighboring farmsor farms having similar characteristics, similar contextual information,and the like. The similarity may be based on a farmer reliability, waterpoint reliability, and the like.

In one example embodiment, the optimal time to pick the crop isdetermined based on a time-series evaluation, a prediction of crop gradefrom video or photographic images using deep learning and visualanalytics, human inspection, economic conditions, and the like. Forexample, the picking of the crop may be delayed if prices are rising orexpedited if prices are falling. The knowledge base (represented usingontology and a searchable graph) may be used to provide ground evidencegiven the crop growth state. In one example embodiment, the knowledgebase hierarchically characterizes and organizes a crop based on croptype, size, calyx characteristics, color development, fruit ripening,and the like. In one example embodiment, the time-series evaluation isbased on a collection of historical observations of farm data,identifying the nature of the growth pattern represented by the sequenceof observations, and then predicting the optimal time to pick the cropin a given time period. The analysis further separates the temporalaspects of the observed farm/crop data in terms of trends, seasonalvariation (e.g., weather pattern, disease pattern, and the like), andassessments of irregular cycles (e.g., short-term market fluctuationsand the like). Various known time series techniques can be used, fromsimple statistical models (for time-series forecasting) to more complexmodels or approaches such as Generalized Autoregressive ConditionalHeteroskedasticity (GARCH), Bayesian-based models, variants of neuralnetwork models (such as Neural Networks Autoregression (NNAR)), variantsof deep learning (such as Recurrent Neural Network (RNN)), LongShort-Term Memory (LSTM), and the like. In one example embodiment, atime series of photographs might show that the rate of change of thecolor or size of a crop of tomatoes is slowing down, indicating that thecrop is near maturation.

In one example embodiment, low magnification photographic images of anentire crop field are processed and a crop analyzer applies variousmetadata extraction tools and analytics algorithms. The metadataextraction tools extract or detect, for example, an identity of the partor location of the farm where the photographic image was taken (e.g.,“southwest corner”). The analytics algorithms learn, for example, themeaning of the rate of change of crop color based on historical imagesand based on the data from the crop knowledge graph.

In one example embodiment, the photographic images of the crops are usedto assess the health of the entire crop. For example, the photographicimages of the crops can be analyzed to recognize a soil condition in onecorner of the field that is poor and that is causing a slower maturingrate (than an average maturing rate) at that position on the farm. Inone example embodiment, the crop analysis further incorporates soil pHvariation or evolution (such as, between a current time Tc and anaggregated past time Tp), farmer history, farm history, and the like. Inanother example embodiment, the crop analysis incorporates soil moisturevariation in different portions of the entire crop.

In one example embodiment, an interactive user interface is providedsuch that a user (such as the owner or manager of the farm) may visuallyobserve the farm conditions and submit visual and other observations(such as a leaf color changing to yellow, particular insects on cropleaves, context information (such as a time of the day and a type/nameor characteristic of the insect), and the like) to the crop analysissystem.

In one example embodiment, actions are recommended, triggered,performed, or any combination thereof using a stored knowledge base andhistoric events related to the crop. Example actions include, but arenot limited to, applying chemicals (such as pesticides, herbicides,insecticides, fungicides, and the like), obtaining and ingestingadditional photographic images of the crops, additional watering orreductions of the watering, recommending to harvest the crop (such asbased on market readiness information and a weather forecast), and thelike. The actions also include sending a signal to an irrigation system,sending a notification to a human expert (such as an agronomist,extension field officer, so-called “Mechanical Turks” and the like),sending an alert message (via SMS, voice over IP messaging,multi-purpose messaging, social media platforms, and the like). In oneexample embodiment, a recommendation may be issued recommending a delayin the ripening of a crop based, for example, on market conditions andother contextual information. For example, if the local market isflooded with tomatoes and prices are low, the ripening may be delayedby, for example, depriving the tomatoes of water for a few days untilmarket conditions improve; harvesting the tomatoes and placing them incold storage; and the like.

FIG. 3B is an illustration of an example system 300 for analyzing andmanaging crops, in accordance with an example embodiment. In one exampleembodiment, the system 300 includes one or more applications 304, afeature extractor 308, a feature analyzer 312, a health/quality assessor316, a grade estimator 320, a crop knowledge graph 380, a productivityforecaster 324, a crop risk analyzer 328, a blueprint generator 332, analert and notification unit 336, and a crop analytics database 344. Thecrop analytics database 344 stores, for example, a crop database,historical crop yield data, disease history, farm and farmer models, andthe like.

The feature extractor 308 obtains photographic or video images of cropsand identifies or extracts crop related features from the images, suchas crop size, crop color, visual spots, virus/insects, and the like, asdescribed more fully above. For example, the low magnification imagesmay show that the crops are maturing at different rates within differentlocations within the farm. Alternatively, a high magnification imagemight show an insect infestation and enable the identity of the insectsto be determined.

The feature analyzer 312 analyzes the features extracted by the featureextractor 308. The feature analyzer 312 searches for the appropriate oroptimal feature combination for determining crop health status. In oneexample embodiment, the feature analyzer 312 is implemented using agenetic algorithm by constructing a maximum entropy based classifier.

The health/quality assessor 316 determines a crop health status usingthe crop related features, environmental context, a farm cohort, thecrop knowledge graph 380, weather, plant status, soil conditions, visualevidence, reports from experts, the nature of an insect or virusinfecting a crop at various locations within a farm, crop stress levels,and the like and generates or updates a crop blueprint. As describedabove, the assessment of the health of the crop can use a decision treeinduction technique to generate new classification rules based on ananalysis of the crop features, where each rule set is given as aclassifier. In one example embodiment, the health/quality assessor 316assesses or predicts the quality Q of the crops within a threshold riskR (such as Q is less than the expected quality Q(s) for the growthstages S). In one example embodiment, the health/quality assessor 316recommends, triggers, or performs actions (or any combination thereof),as described more fully above. Some embodiments utilize, for example, arecurrent neural network (RNN) including Long short-term memory (LSTM)units.

The grade estimator 320 estimates the grade of a crop based, forexample, on the size of the crop, color of the crop, spots on the crop,the crop knowledge graph 380, and the like. In one example embodiment,the crop grading also assesses the impact of the grading on thepredicted market value of the crop, taking into consideration the localmarket conditions (supply and demand) of where the crops are expected tobe sold. As described above, in one example embodiment, the method ofdetermining or predicting the crop grading and determining or predictingthe optimal time to harvest the crop utilizes learning algorithms anddatabases of neighboring farms or farms having similar characteristics,similar contextual information, and the like. The similarity may bebased on a farmer reliability, water point reliability, and the like.

The productivity forecaster 324 estimates the productivity for a cropbased on the crop knowledge graph 380, water uptake patterns, socialcharacteristics, crop type, visual analytics (to, for example, detectdisease and insect infestation), weather, and the like. In one exampleembodiment, the crop risk analyzer 328 is based on training at least onemachine learning model (e.g., from a Decision Tree and Bayesian networkmodeling to deep neural network algorithms) using features extractedfrom the crop knowledge graph, water uptake patterns, socialcharacteristics, crop type, visual analytics, weather, and the like. Thecrop risk analyzer 328 orders the features by value by running a scoringfunction S that measures feature-relevance affecting the current andpredicted crop risk, and then selecting (feature selection) the khighest ranked features according to S (a high score is indicative of avaluable relevant feature). Various methods that are known in the artfor ranking criteria, such as the Pearson correlation coefficientalgorithm, can be used. The generated risk score value can be high,medium, or low compared to a preconfigured threshold value. Thepreconfigured threshold value can be learned from historically bestperforming threshold values. In one or more embodiments, the output ofthe Crop Risk Analyzer 328 is used by the productivity forecaster.

The alert and notification unit 336 issues recommendations to usersbased on results generated by the system 300. As described above, therecommendations may suggest that a farmer pick crops early, may warn ofthe detection of blight or insects infesting the crops and thereforerecommend spraying a pesticide, may suggest a rotation to another cropin the following growing season, and the like. In one exampleembodiment, the alert and notification unit 336 obtains the resultsgenerated by the health/quality assessor 316, the grade estimator 320,the productivity forecaster 324, the crop risk analyzer 328, farmdetails (including the farmer details from a farm profile/blueprint andprevious recommendations issued to the farmer), and the like andgenerates one or more recommendations and a set of actions based on theobtained results. In one example embodiment, the alert and notificationunit 336 is heuristic or rule based, implements statistical or machinelearning algorithms (such as time-aware collaborative filteringtechniques and neural network based algorithms), and the like. Thegenerated recommendations along with recommended actions can be furtheroptimized based on context information, such as weather, farmer specificconditions, and the like.

The blueprint generator 332 generates blueprints for various crops, suchas the crop blueprint 390 for a tomato crop. In one example embodiment,the blueprint is generated or inferred based on data from a cropdatabase.

The applications 304 include, for example, an interactive user interfacethat provides the ability for a user to submit features and farmconditions that are visually or otherwise observed. For example, a humanobserver may submit visual observations 340 regarding crop health viaone of the applications 304, such as the interactive user interface. Theinteractive user interface provides the ability for a user to receivereports and recommendations, such as crop health status, crop grade,actions/recommendations, and the like. In one example embodiment, a cropblueprint is presented via the interactive user interface. Theinteractive user interface may obtain the content of the crop blueprintsand the like from a cloud-based server. In one example embodiment, thecloud-based server provides information using Hyper-text Markup Language(HTML).

FIG. 4 is a flowchart of an example method 400 for generating orupdating a crop blueprint, in accordance with an example embodiment. Themethod 400 is also capable of recommending, triggering, performing, orany combination thereof one or more actions. In one example embodiment,the method 400 is performed by the system 300.

Initially, one or more photographic images of crops are obtained(operation 404). In one example embodiment, operation 404 is performedat regular time intervals, at a regular interval of the crop growthstages, in a continuous manner, and the like. The images may be obtainedfrom a mobile phone/device, a fixed camera/sensor, a flying drone, asatellite, and the like.

One or more crop related features are identified or extracted from theone or more images (operation 408). For example, hyper-spectral imagingmay be used to detect early blight conditions. In one exampleembodiment, low magnification images of an entire field are processedand various metadata extraction tools and analytics algorithms areapplied. The metadata extraction tools can, for example, extract ordetect an identity of the part or location of the farm where the imagewas taken. In another embodiment, high magnification images of the cropsfor a portion of the field may be used to detect insects.

A crop health status is determined using the one or more features, anenvironmental context, and a farm cohort (operation 412). The crophealth status may be based on crop features, crop stress levels,environmental context (such as weather), number and types of insects,crop knowledge graph models, and the like. The environmental context is,for example, the local weather, a location of the crops, and the like.

A crop blueprint is generated or updated (operation 416). In one exampleembodiment, a crop blueprint is generated or inferred based on data froma crop database. The blueprint includes, for example, computed valuessuch as the health of the crop, the assessed growth stage of the crop,implications of the environment on the health of the crop, and the like.

In one example embodiment, the quality Q of the crops within a thresholdrisk R (such as Q is less than the expected quality Q(s) for the growthstages (S) is assessed or predicted (operation 420). In one exampleembodiment, the quality assessment or prediction is performed bytraining a machine learning model using historical data. The historicaldata may be collected from, for example, a quality metric over a periodof time that is determined by a human.

In one example embodiment, one or more actions are recommended,triggered, performed, or any combination thereof (operation 424). Theaction may be triggered by a condition, such as the health or quality ofthe crop in relation to an expected health or quality level L for thecurrent stage of crop growth. The actions include, but are not limitedto, notifying one or more human assessors of the crop status based onthe estimation or prediction, informing the farmer of criticalproduction risks, supporting farmer differentiation, providing a croprating at harvest time, recommending harvesting of the crops early,recommending a switch to a different crop for the following growingseason, recommending the application of chemicals (such as pesticides,herbicides, insecticides, fungicides, and the like), recommendingobtaining and ingesting more photographic images of crops, recommendingto harvest the crop (such as based on market readiness information,predicted weather forecast, and the like), triggering an irrigationsystem, sending a notification to a human expert (such as an agronomist,extension field officer, so-called “Mechanical Turk,” and the like),sending an alert (such as via SMS, voice over IP messaging,multi-purpose messaging, social media platforms, and the like; and suchas alerting farmers of a community based on the type and risk of adetected event), recommending a delay of the ripening period based onmarket conditions and other contextual information (such as, if themarket is flooded with tomatoes and prices are low, recommendingdepriving the tomatoes of water for a few days until market conditionsimprove), and the like. The actions are based, for example, on the localweather, plant status, human evaluators, farmer profile, and the like.In one example embodiment, an action is recommended using a storedknowledge base, historic events of the crop, and the like.

In one example embodiment, the features extracted from the photographicimages include, but are not limited to, a particular shape of the crop;a crop color and a rate of change of crop color; visual spots and spotpatterns on the crop; an identification, an assessment, acharacterization, or any combination thereof of an insect infesting thecrop; an identification, an assessment, a characterization, or anycombination thereof of a virus contracted by the crop; a pattern ofworms/insects infesting a crop; an identification on how worms/insectsare spreading to other plants on the farm over time; a condition ofsoil; and the like. The soil condition is recognized, for example, byobserving a slower maturing rate (than the average maturing rate) of acrop at a particular location on the farm. In one example embodiment,the images are obtained via a high quality camera and hyper-spectralimaging is used for early blight detection. In another exampleembodiment, the photographic images are obtained by a camera that cantake images at low magnification, high magnification, or both.

In one example embodiment, the features extracted in operation 408 areaugmented with features and farm conditions that are visually orotherwise observed and submitted to the system by a user via a userinterface. The observations are, for example, the rate of change of leafcolor, particular insects on crop leaves, context information (such as atime of the day, and a type/name/number or characteristic of theinsect), and the like.

FIG. 5 is a flowchart of an example method 500 for determining orpredicting a grade of a crop, in accordance with an example embodiment.The rating (grade) can be used to inform the farmer of criticalproduction risks, to support farmer differentiation, and the like. Insome instances, with the informed consent of the farmer, a banker orother party with a legitimate interest in the farm may be provided withaccess to the data to influence the decision on harvesting, to gainconfidence in the farmer for purposes of extending credit, and the like.In one example embodiment, a report regarding a grade of a crop isobtained from a human observer (operation 504). The determination ofcrop grading may be performed, for example, by a human expert (such asan agronomist). In one example embodiment, the determination of cropgrading is performed via collaboration with human reporters connectedvia a telecommunications network. The grading (quality) rating is basedon an understanding of weather, soil fertility, farm managementpractices, a comparison to a crop without imperfections, and the like.In one example embodiment, the report is generated by providing a videostream to a remote professional for expert evaluation of the crop. Inanother example embodiment, the report is generated by providing aseries of static photographic images to a remote professional for expertevaluation of the crop.

In one example embodiment, an expert system determines a crop grading(operation 508). For example, learning methods, such as deep learningand neural networks, can be applied to generate an automated growthstage analysis. The learning methods can be applied with a confidencelevel (L). If the L is less than a certain specified threshold value,appropriate actions will be taken. For example, the meaning of the ratechange of color changes may be learned based on historical images andbased on the data from the crop knowledge graph. The threshold value canbe learned over a period of time using historical threshold values. Inone example embodiment, the automated growth stage analysis uses contextinformation, such as weather and the like. In one example embodiment,databases of neighboring farms or farms having similar characteristics,similar contextual information, and the like are used to determine therating of the crop.

In one example embodiment, the expert system determines a crop ratingby, for example, analyzing photographic images of the crops, analyzingweather conditions, and the like. For example, low magnification imagesof an entire field can be processed by various metadata extraction toolsand analytics algorithms. The metadata extraction tools can, forexample, extract or detect the identity of the part or location of thefarm where the image was taken. In another embodiment, highmagnification photographic images can be used to determine the type andnumber of insects.

In one example embodiment, the optimal time to harvest a crop isdetermined (operation 512). For example, learning algorithms, visualanalytics, and databases of neighboring farms or farms having similarcharacteristics, similar contextual information, and the like can beused to determine the optimal time to harvest a crop. The similarity maybe based on a water sensitivity score, farmer reliability, a water pointreliability, and the like.

In one example embodiment, the optimal time to harvest the crop isdetermined based on a time-series evaluation. The knowledge base(represented using ontology and searchable graphs) may be used toprovide ground evidence of the crop growth state. In one exampleembodiment, the knowledge base hierarchically characterizes andorganizes a crop based on crop type, crop size, calyx characteristics,color development, fruit ripening, and the like.

In one example embodiment, the impact of crop grading on the predictedmarket value of the crop is assessed (operation 516). This is based, forexample, on historical market data for graded crops, cohort of crops, orboth.

In one example embodiment, the analysis of method 500 considers theplanting history of the farm, a water uptake history of the farm overthe last N days, past farmer activities, and the like. The analysis mayfurther incorporate soil pH variation or evolution (between, forexample, a current time Tc and an aggregated past time Tp), farmerhistory, and farm history.

Given the discussion thus far, it will be appreciated that, in generalterms, an exemplary method, according to an aspect of the invention,includes the operations of obtaining one or more images of crops 404; atleast one of identifying or extracting 308, 312 one or more crop relatedfeatures from the one or more images 408; determining 316 a crop healthstatus based on the one or more crop related features, an environmentalcontext, a growth stage of the crop, and a farm cohort by using acomputerized deep learning system to perform an automated growth stageanalysis 412; and at least one of recommending 336, triggering, andperforming one or more actions 424.

In one example embodiment, the operations further comprise assessing orpredicting a quality Q of the crops within a threshold risk R 316, 420.In one example embodiment, the operations further comprise generating orupdating 332 a crop blueprint 390, 416. In one example embodiment, theoperations further comprise augmenting the crop related features withfarm conditions that are observed and submitted by a user 340, 504 via auser interface. In one example embodiment, the operations furthercomprise analyzing 312 the one or more images of crops to detect anidentity of a location of a farm corresponding to one of the one or moreimages. In one example embodiment, the operations further compriseperforming hyper-spectral imaging to recognize early blight conditions.

In one example embodiment, the crop health status is based on one ormore of the crop related features, crop stress levels, environmentalcontext, and crop knowledge graph models 380. In one example embodiment,the one or more images of crops are obtained from a mobile phone,mobile, device, a camera, a sensor, a sensing device mounted on a farmvehicle, a flying drone, or a satellite. In one example embodiment, therecommending, triggering, or performing the one or more actions istriggered by a health or quality of the crop in relation to an expectedlevel L for a current stage of crop growth. In one example embodiment,the actions are one or more of notifying one or more human assessors 336of a crop status based on the crop health status, informing 336 a farmerof critical production risks, supporting farmer differentiation,providing a crop rating at harvest time, recommending 336 harvesting ofthe crops early, recommending 336 a switch to a different crop for afollowing growing season, recommending 336 application of chemicals orfertilizers, recommending 336 obtaining and ingesting more crop images,recommending to harvest the crop, triggering an irrigation system,sending a notification 336 to a human expert, sending an alert 336, andrecommending 336 a delay of a ripening period based on market conditionsand other contextual information. In one or more embodiments, furthersteps include actually carrying out one or more recommended actions;e.g., harvesting the crops early, switching to the different crop forthe following growing season, applying the chemicals or fertilizers,harvesting the crop, and irrigating with the irrigation system. In somecases, actions can be controlled or triggered via network adapter 20and/or I/O interfaces 22 discussed elsewhere herein.

In one example embodiment, an action is recommended using a storedknowledge base and historic events 344 of the crop. In one exampleembodiment, the crop related features are one or more of a particularshape of the crop; a rate of change of crop color; spots and spotpatterns on the crop; an identification, an assessment, acharacterization, or any combination thereof of an insect infesting thecrop; an identification, an assessment, a characterization, or anycombination thereof of a virus contracted by the crop; a pattern ofworms/insects infesting the crop; an identification on how worms orinsects are spreading to other plants in a farm over time; and acondition of soil. In one example embodiment, the one or more images ofcrops are photographic images at different levels of magnification.

In one example embodiment, a system for determining or predicting agrade of a crop comprises a memory 28; and at least one processor 16,coupled to the memory, and operative to obtain a report regarding afirst grade of a crop from a user 504; determine a second grade of thecrop from an expert system 508 using deep learning to analyze weatherconditions and one or more images of the crop; determine an optimal timeto harvest the crop 512; and assess an impact of the crop grading on apredicted market value of the crop 516.

In one example embodiment, a non-transitory computer readable mediumcomprising computer executable instructions which when executed by acomputer cause the computer to perform the method of obtaining one ormore images of crops 404; at least one of identifying or extracting 308one or more crop related features from the one or more images 408;determining 316 a crop health status based on the one or more croprelated features, an environmental context, a growth stage of the crop,and a farm cohort using deep learning to perform an automated growthstage analysis 412; and at least one of recommending 336, triggering,and performing one or more actions 424.

One or more embodiments of the invention, or elements thereof, can beimplemented in the form of an apparatus including a memory and at leastone processor that is coupled to the memory and operative to performexemplary method steps. FIG. 6 depicts a computer system that may beuseful in implementing one or more aspects and/or elements of theinvention, also representative of a cloud computing node according to anembodiment of the present invention. Referring now to FIG. 6, cloudcomputing node 10 is only one example of a suitable cloud computing nodeand is not intended to suggest any limitation as to the scope of use orfunctionality of embodiments of the invention described herein.Regardless, cloud computing node 10 is capable of being implementedand/or performing any of the functionality set forth hereinabove.

In cloud computing node 10 there is a computer system/server 12, whichis operational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 6, computer system/server 12 in cloud computing node 10is shown in the form of a general-purpose computing device. Thecomponents of computer system/server 12 may include, but are not limitedto, one or more processors or processing units 16, a system memory 28,and a bus 18 that couples various system components including systemmemory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, and external disk drivearrays, RAID systems, tape drives, and data archival storage systems,etc.

Thus, one or more embodiments can make use of software running on ageneral purpose computer or workstation. With reference to FIG. 6, suchan implementation might employ, for example, a processor 16, a memory28, and an input/output interface 22 to a display 24 and externaldevice(s) 14 such as a keyboard, a pointing device, or the like. Theterm “processor” as used herein is intended to include any processingdevice, such as, for example, one that includes a CPU (centralprocessing unit) and/or other forms of processing circuitry. Further,the term “processor” may refer to more than one individual processor.The term “memory” is intended to include memory associated with aprocessor or CPU, such as, for example, RAM (random access memory) 30,ROM (read only memory), a fixed memory device (for example, hard drive34), a removable memory device (for example, diskette), a flash memoryand the like. In addition, the phrase “input/output interface” as usedherein, is intended to contemplate an interface to, for example, one ormore mechanisms for inputting data to the processing unit (for example,mouse), and one or more mechanisms for providing results associated withthe processing unit (for example, printer). The processor 16, memory 28,and input/output interface 22 can be interconnected, for example, viabus 18 as part of a data processing unit 12. Suitable interconnections,for example via bus 18, can also be provided to a network interface 20,such as a network card, which can be provided to interface with acomputer network, and to a media interface, such as a diskette or CD-ROMdrive, which can be provided to interface with suitable media.

Accordingly, computer software including instructions or code forperforming the methodologies of the invention, as described herein, maybe stored in one or more of the associated memory devices (for example,ROM, fixed or removable memory) and, when ready to be utilized, loadedin part or in whole (for example, into RAM) and implemented by a CPU.Such software could include, but is not limited to, firmware, residentsoftware, microcode, and the like.

A data processing system suitable for storing and/or executing programcode will include at least one processor 16 coupled directly orindirectly to memory elements 28 through a system bus 18. The memoryelements can include local memory employed during actual implementationof the program code, bulk storage, and cache memories 32 which providetemporary storage of at least some program code in order to reduce thenumber of times code must be retrieved from bulk storage duringimplementation.

Input/output or I/O devices (including but not limited to keyboards,displays, pointing devices, and the like) can be coupled to the systemeither directly or through intervening I/O controllers.

Network adapters 20 may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

As used herein, including the claims, a “server” includes a physicaldata processing system (for example, system 12 as shown in FIG. 6)running a server program. It will be understood that such a physicalserver may or may not include a display and keyboard.

One or more embodiments can be at least partially implemented in thecontext of a cloud or virtual machine environment, although this isexemplary and non-limiting. Reference is made back to FIGS. 1-2 andaccompanying text.

It should be noted that any of the methods described herein can includean additional step of providing a system comprising distinct softwaremodules embodied on a computer readable storage medium; the modules caninclude, for example, any or all of the appropriate elements depicted inthe block diagrams and/or described herein; by way of example and notlimitation, any one, some or all of the modules/blocks and orsub-modules/sub-blocks described. The method steps can then be carriedout using the distinct software modules and/or sub-modules of thesystem, as described above, executing on one or more hardware processorssuch as 16. Further, a computer program product can include acomputer-readable storage medium with code adapted to be implemented tocarry out one or more method steps described herein, including theprovision of the system with the distinct software modules.

One example of user interface that could be employed in some cases ishypertext markup language (HTML) code served out by a server or thelike, to a browser of a computing device of a user. The HTML is parsedby the browser on the user's computing device to create a graphical userinterface (GUI).

Exemplary System and Article of Manufacture Details

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method for generating or updating a cropblueprint, the method comprising: obtaining one or more images of crops;at least one of identifying or extracting one or more crop relatedfeatures from the one or more images; determining a crop health statusbased on the one or more crop related features, an environmentalcontext, a growth stage of the crop, and a farm cohort by using acomputerized deep learning system to perform an automated growth stageanalysis; and at least one of recommending, triggering, and performingone or more actions.
 2. The method of claim 1, further comprisingassessing or predicting a quality Q of the crops within a threshold riskR.
 3. The method of claim 1, further comprising generating or updating acrop blueprint.
 4. The method of claim 1, further comprising augmentingthe crop related features with farm conditions that are observed andsubmitted by a user via a user interface.
 5. The method of claim 1,further comprising analyzing the one or more images of crops to detectan identity of a location of a farm corresponding to one of the one ormore images.
 6. The method of claim 1, further comprising performinghyper-spectral imaging to recognize early blight conditions.
 7. Themethod of claim 1, wherein the crop health status is based on one ormore of the crop related features, crop stress levels, environmentalcontext, and crop knowledge graph models.
 8. The method of claim 1,wherein the one or more images of crops are obtained from a mobilephone, mobile, device, a camera, a sensor, a sensing device mounted on afarm vehicle, a flying drone, or a satellite.
 9. The method of claim 1,wherein the recommending, triggering, or performing the one or moreactions is triggered by a health or quality of the crop in relation toan expected level L for a current stage of crop growth.
 10. The methodof claim 1, wherein the actions are one or more of notifying one or morehuman assessors of a crop status based on the crop health status,informing a farmer of critical production risks, supporting farmerdifferentiation, providing a crop rating at harvest time, recommendingharvesting of the crops early, recommending a switch to a different cropfor a following growing season, recommending application of chemicals orfertilizers, recommending obtaining and ingesting more crop images,recommending to harvest the crop, triggering an irrigation system,sending a notification to a human expert, sending an alert, andrecommending a delay of a ripening period based on market conditions andother contextual information.
 11. The method of claim 1, wherein anaction is recommended using a stored knowledge base and historic eventsof the crop.
 12. The method of claim 1, wherein the crop relatedfeatures are one or more of a particular shape of the crop; a rate ofchange of crop color; spots and spot patterns on the crop; anidentification, an assessment, a characterization, or any combinationthereof of an insect infesting the crop; an identification, anassessment, a characterization, or any combination thereof of a viruscontracted by the crop; a pattern of worms/insects infesting the crop;an identification on how worms or insects are spreading to other plantsin a farm over time; and a condition of soil.
 13. The method of claim 1,wherein the one or more images of crops are photographic images atdifferent levels of magnification.
 14. A system for determining orpredicting a grade of a crop, the system comprising: a memory; and atleast one processor, coupled to said memory, and operative to: obtain areport regarding a first grade of a crop from a user; determine a secondgrade of the crop from an expert system using deep learning to analyzeweather conditions and one or more images of the crop; determine anoptimal time to harvest the crop; and assess an impact of the cropgrading on a predicted market value of the crop.
 15. The system of claim14, wherein the determination of crop grading is performed viacollaboration with human reporters connected via a telecommunicationsnetwork.
 16. The system of claim 14, wherein the crop grading and theoptimal time to harvest the crop are based on databases of farms havingsimilar characteristics, similar contextual information, or both. 17.The system of claim 14, wherein the crop grading and the optimal time toharvest the crop are based on a planting history of a farm, a wateruptake history of the farm over a last N days, past farmer activities,and a soil pH variation or evolution.
 18. The system of claim 14,wherein the expert system applies learning methods with a confidencelevel (L) to generate an automated growth stage analysis.
 19. The systemof claim 14, wherein the optimal time to harvest the crop is determinedbased on a time-series evaluation.
 20. A non-transitory computerreadable medium comprising computer executable instructions which whenexecuted by a computer cause the computer to perform the method of:obtaining one or more images of crops; at least one of identifying orextracting one or more crop related features from the one or moreimages; determining a crop health status based on the one or more croprelated features, an environmental context, a growth stage of the crop,and a farm cohort using deep learning to perform an automated growthstage analysis; and at least one of recommending, triggering, andperforming one or more actions.